基于辅助粒子滤波算法的车辆行驶状态和参数联合估计方法研究

The Research of State and Parameter Estimation Under Driving Situation Based on Auxiliary Particle Filter Method

汽车行驶状态及关键参数的联合估计对于汽车主动控制以及新型结构的验证具有重要意义。介绍了一种应用辅助粒子滤波技术实现车辆行驶状态、参数联合估计的控制算法。该算法以包含定常统计特性噪声的汽车动力学模型为基础,利用龙格—库塔方法模拟汽车动力学模型,同时引入辅助变量,通过二次加权操作使得粒子权重变化趋于稳定,最终实现了行驶状态和关键参数的联合估计,仿真结果表明,辅助粒子滤波可以有效的提高标准粒子滤波算法的精度。

The estimation of vehicle state and key parameters estimation has important significance for the vehicle active safety control and new structure design. A control algorithm using auxiliary particle filter to estimate the vehicle driving state and parameters was suggested. The algorithm was based on vehicle dynamics system containing constant noise and non-linear model,and Runge-Kuttta method was used to simulate the model. An assigned variable was brought into the algorithm. With two rounds weighted processes,the weights of the samples changed more smoothly. The local state and parameters were identified together. The comparison results demonstrated that the proposed algorithm can estimate vehicle state and key parameters accurately and improve the accuracy of standard particle filter.